The Internet provides a large collection of interlinked content items in various formats, including documents, images, video and other media content. As the Internet has grown, the ability of users to search this collection and identify content items relevant or responsive to a given query has become increasingly difficult. The vast number of content items available on the Internet has led to frustration on the part of users attempting to locate information that is relevant to their informational needs. Accordingly, search engines have been developed to facilitate the information retrieval process.
A search engine processes and indexes content items available on the Internet. To find a desired or relevant content item, a user enters a query term or a set of query terms through a search interface. The search engine receives the query and searches an index for known content items that are associated with or otherwise match the term or terms. The search engine then identifies a set of content items that are relevant to the submitted query, returning the set to the user, a search result set. The search result set usually comprises a list, ranked by relevance, of one or more content items that are responsive to the query term or terms received.
The earliest search engines returned search results from a network, e.g. the Internet, without any regard to any specific categories into which those search results could fit. As search engines evolved, certain content categories, called “verticals,” became recognized. Given a corpus of content, a “vertical” is a subset of content items that satisfy some criteria associated with one or more content items. For example, one vertical recognized by the Yahoo! Internet search engine is the “local” vertical, which consists of content associated with a given geographic area, such as New York, San Francisco, London, etc. Additionally, a vertical may comprise a corpus of related content items available from a third party data store, e.g. a web site hosting content items for a given topic
The Yahoo! Internet search engine allows a user to specify, in addition to a set of query terms, a specific vertical in which the user would like to conduct a search for content. The verticals from which a user of the Yahoo! Internet search engine may select include, for example, “video,” “images,” “local,” “shopping,” “answers,” “audio,” “directory,” “jobs,” and “news.” Verticals may also comprise, as indicated above, third party web sites not affiliated with a given search engine, such as Flickr, Upcoming, Yahoo Buzz, etc. The default search, conducted on an overall index, may return a large, unfocused set of content items or search results instead of a more responsive and focused search result from a specific vertical such as “local.” A focused search result from a specific vertical may comprise data sets with an established structure (such as key value pairs, contact, location, information prices, images, etc.) and may be operative facilitate a comparison between individual results. A result set from a specific vertical is also helpful when the user knows the particular category of search results that is of interest. When the user knows the particular category ahead of time, the user may save time by conducting the search on the specific vertical instead of scouring the entire Internet.
Unfortunately, many users default to a search on an entire corpus of content items and are unaware of or have never performed a more focused “vertical” based search. This may be a result of the fact that finding the specific vertical that may contain the content items a user is looking for is not intuitive. Indeed, most users do not possess the technical awareness or even the time to determine if a specific vertical is more likely to return a responsive result set. Since users generally stop looking if the particular content item of interest is not returned within the first two search result sets, the more relevant and responsive content items on the Internet continue to remain hidden.
The process of searching for relevant content items on a network is therefore time consuming and complex. Accordingly, there exists a need for systems, methods and computer program products for presenting suggestions of vertical segments that are relevant to a query in an organized and categorized fashion.